Preliminary validation of automated production analysis of feller buncher operations: integration of onboard computer data with LiDAR inventory

This study examines the development and preliminary validation of a protocol for fully automated production analysis in forest harvesting operations, utilizing onboard computer data. By integrating ignition status, motion status, and machine location data from FPDat II data loggers with LiDAR forest...

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Bibliographic Details
Published in:European journal of forest research 2024-12, Vol.143 (6), p.1819-1833
Main Authors: Lahrsen, Steffen, Mologni, Omar, Liu, Zexi, Röser, Dominik
Format: Article
Language:English
Subjects:
Airborne/spaceborne computers
Automation
Biomedical and Life Sciences
Data analysis
Data collection
Digital computers
Forest harvesting
Forestry
Lidar
Life Sciences
Plant Ecology
Plant Sciences
Productivity
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Summary:This study examines the development and preliminary validation of a protocol for fully automated production analysis in forest harvesting operations, utilizing onboard computer data. By integrating ignition status, motion status, and machine location data from FPDat II data loggers with LiDAR forest inventory data, this research aims to accurately predict key production metrics such as productive time, area covered, volume harvested, and overall productivity for individual machines. The efficacy of this fully automated data collection and analysis approach is scrutinized using a direct comparison with traditional in-field data collection methods, with a focus on feller buncher operations. Findings indicate minimal discrepancies in productive time recordings (0.9%) and area covered by machines (-1.9%), with slightly larger discrepancies observed in volume harvested (-4.4%) and productivity (-5.3%). More significant disparities in area coverage estimations were noted during individual shifts, particularly when multiple machines operated simultaneously or when there was incomplete coverage of machine tracking by FPDat II data loggers. This study is a crucial step towards understanding the capabilities and limitations of onboard computer data for remote production analysis in forest operations. Through comprehensive analysis, it contributes to the digital transformation of forestry, underscoring both the challenges and opportunities of automated tools in enhancing harvesting efficiency.
ISSN:1612-4669
1612-4677
DOI:10.1007/s10342-024-01732-7